# model-deployment The machine-learning practice of deploying a trained, evaluated model to production so it can provide predictions or inferences. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## Chip Huyen explains how to cut inference costs without new hardware DevFeed: [Chip Huyen explains how to cut inference costs without new hardware]() Original publisher: [Read original article]() Author: Tim Koopmans Published: 2026-09-13T15:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [Inference Performance](), [Low-Latency Inference](), [model-deployment](), [Optimization](), [Frontier Model](), [AI Engineering](), [Latency](), [math]() Tags: [ai](), [ai-agents](), [ai-engineering](), [ai-infrastructure](), [frontier-model](), [inference](), [low-latency](), [metrics](), [optimization](), [performance](), [post-contributed](), [reasoning](), [scylladb](), [sponsor-scylladb](), [sponsored](), [sponsored-post-contributed](), [tokens]() ### AI overview Chip Huyen explains why inference costs can outweigh one-time frontier-model training costs and outlines ways to optimize inference without new hardware. The article emphasizes latency metrics such as time to first token, time per output token, end-to-end latency, and goodput, especially for reasoning models. ### Source excerpt Last October, the P99 conference -- the online gathering for developers focused on high-performance, low-latency applications -- featured a cracking The post Chip Huyen explains how to cut inference costs without new hardware appeared first on The New Stack. ## Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU DevFeed: [Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU]() Original publisher: [Read original article]() Author: Anthony Su; Injae Kwak Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Embeddings](), [vllm](), [Inference](), [long-context](), [autoscaling](), [Kubernetes](), [Cloud](), [Google](), [Artificial Intelligence](), [model-deployment](), [Open Source]() Tags: [autoscaling](), [embedding](), [github](), [gpu](), [inference](), [kubernetes](), [long-context](), [multimodal](), [open-source](), [retrieval](), [tpu](), [vllm]() ### AI overview This article explains how Google Cloud integrated native TPU support into vLLM to serve long-context, multimodal embedding models at production scale. It describes elastic TPU provisioning with Google Kubernetes Engine, autoscaling across accelerator types, and TPU-specific optimizations for long sequences and chunked prefill. The resulting setup is designed to preserve numerical parity with GPU reference baselines while supporting high-throughput semantic retrieval applications. ### Source excerpt Google Cloud has natively integrated TPU support into the vLLM serving engine, allowing developers to elastically scale high-demand embedding pipelines using Google Kubernetes Engine (GKE). To handle massive 15K+ token contexts for models like Qwen3-Embedding-8B, the engineering team implemented TPU-specific optimizations such as hardware-safe tensor alignment, JAX/XLA compilation pre-warming, and a hybrid StepPool architecture for chunked prefill management. These enhancements achieve near-perfect numerical parity with reference GPU baselines, and developers can immediately leverage the open-sourced setup recipes on the AI-Hypercomputer GitHub to build their own high-throughput semantic retrieval applications. ## Building Pinterest's VLM Serving Stack on NVIDIA Dynamo DevFeed: [Building Pinterest's VLM Serving Stack on NVIDIA Dynamo]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-10T23:08:16Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [vlm](), [Inference](), [model-deployment](), [Orchestration](), [Latency](), [Caching](), [Open Source Models & Datasets]() Tags: [blackwell](), [cache](), [dynamo](), [embeddings](), [engineering](), [inference](), [latency](), [llm](), [multimodal](), [multimodal-ai](), [nvidia](), [nvidia-blackwell](), [open-source](), [orchestration](), [performance](), [pinterest](), [vllm](), [vlm](), [vlm-serving]() ### AI overview Pinterest describes its VLM serving stack built on NVIDIA Blackwell GPUs and NVIDIA Dynamo. The stack addresses multimodal inference demands such as image processing, variable prefill costs, KV-cache pressure, routing, and cache offloading. ### Source excerpt Lei Pan | Senior Software Engineer; Salina Wu | Senior Software Engineer; Cristian Lopez | Software Engineer I; Guangtong Bai | Staff Software Engineer; Soam Acharya | Principal Engineer; Saurabh Vishwas Joshi | Principal Engineer; Chia-Wei Chen | Staff Software Engineer; Ambud Sharma | Principal Engineer Why VLM Serving Matters at Pinterest Pinterest is a visual search and discovery platform, so its AI systems must reason over both language and visual content. Vision-language models (VLMs), which can interpret images, compare visual candidates, and respond naturally to user intent, are becoming the foundation for the next generation of Pinterest experiences: Pinterest Assistant, hybrid search, multimodal reranking, content understanding, signal generation, content safety, and more. This direction also reflects Pinterest's broader strategy to customize open-source models to meet its product & scale needs. Pinterest Assistant is a standout example. This multi-turn conversational experience covers both user language and visual content. Serving it requires low-latency VLM inference over rich multimodal context as well as reworking Qwen3-VL with proprietary multimodal embeddings to cut runtime cost while improving performance. Serving VLMs, however, introduces more challenges compared to text-only LLM workloads. Requests may carry multiple images, require extra vision encoder computation, incur larger and more variable prefill cost, and create higher KV cache pressure. To support this new class of models & product experiences, we built Pinterest's VLM serving stack on top of NVIDIA Blackwell GPUs and NVIDIA Dynamo. Blackwell GPUs incorporate many architectural innovations that are uniquely positioned for today's most demanding AI workloads -- including higher BF16/FP8 compute throughput, increased memory bandwidth, and larger HBM memory capacity -- that enable dramatically higher performance for inference. Dynamo provides a distributed inference orchestration layer that g ## Reduce inference cold starts on Amazon SageMaker HyperPod with model caching DevFeed: [Reduce inference cold starts on Amazon SageMaker HyperPod with model caching]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:37:49Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Caching](), [Inference](), [model-deployment](), [Large Language Model](), [autoscaling](), [Kubernetes](), [Amazon S3]() Tags: [amazon](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [autoscaling](), [benchmark](), [caching](), [expert-400](), [inference](), [kubernetes](), [llm]() ### AI overview Amazon SageMaker HyperPod model caching preloads model weights and container images onto cluster nodes, reducing inference-pod cold starts by serving assets from local NVMe storage. ### Source excerpt Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it. ## Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data DevFeed: [Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data]() Original publisher: [Read original article]() Published: 2026-09-10T10:42:55Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [model-deployment](), [Inference](), [data](), [datasets]() Tags: [ai](), [ai-models](), [cloud](), [data](), [enterprise](), [inference](), [on-prem](), [partnership](), [train]() ### AI overview Cloudera and Mistral announce a partnership to deploy and train customized AI models on enterprise data across hybrid, on-premises, cloud, and air-gapped environments while retaining data control. ### Source excerpt Cloudera and Mistral join forces to bring specialized, sovereign AI intelligence to enterprise data, helping regulated industries innovate on their own terms. ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-09T20:31:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Optimization](), [model-deployment](), [multimodal](), [Dynamo](), [Inference Performance](), [Embeddings](), [Mixture of Experts (MoE)](), [GPU](), [Open Source](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [computer-vision-video-analytics](), [developer-tools-techniques](), [dynamo](), [embeddings](), [gpu](), [inference](), [low-latency-inference](), [mixture-of-experts-moe](), [model-serving](), [multimodal](), [nvfp4](), [open-source](), [optimization]() ### AI overview This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time. ### Source excerpt Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... ## Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers DevFeed: [Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers]() Original publisher: [Read original article]() Author: Ananth Raghavendra Published: 2026-09-09T15:51:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [Containers](), [vlm](), [GPU](), [PyTorch]() Tags: [amazon-eks](), [containers](), [gpu](), [inference](), [intermediate-200](), [pytorch](), [technical-how-to](), [vulnerabilities]() ### AI overview The article introduces AWS Ray Serve Deep Learning Containers as a maintained, pre-tested inference option for teams moving from unmaintained TorchServe workloads. It demonstrates deploying a vision-language model on Amazon EKS using a single GPU node. ### Source excerpt TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. ## AI-Ready Private Cloud with Cisco and VMware DevFeed: [AI-Ready Private Cloud with Cisco and VMware]() Original publisher: [Read original article]() Author: sabina anja Published: 2026-09-08T15:33:39Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [Network](), [Cloud](), [networking](), [cloud-infrastructure](), [distributed-training](), [datacenter](), [GPU](), [Latency](), [Inference](), [model-deployment](), [inference-endpoints]() Tags: [ai](), [cisco](), [cloud](), [cloud-infrastructure](), [cloud-platform](), [data-center](), [distributed-training](), [fabric](), [gpu](), [home-page](), [inference](), [inference-endpoints](), [latency](), [networking](), [object-storage](), [pipelines](), [private-cloud](), [telemetry](), [vcf-9-1](), [vcf-networking](), [vmware](), [vmware-cloud-foundation]() ### AI overview This article explains why an AI-ready private cloud requires more than adding GPUs. It focuses on how Broadcom and Cisco are integrating VMware Cloud Foundation with Cisco Nexus One Fabric to address AI workload networking, including bandwidth-intensive east-west traffic, bursty north-south traffic, latency, congestion management, and telemetry across virtual and physical infrastructure. ### Source excerpt An AI-ready private cloud is not simply a private cloud with GPUs added to it. What determines whether a private cloud platform can actually serve AI workloads effectively is everything built around them: how the fabric carries traffic, how the tenancy model lets teams consume capacity, and how policy and telemetry stay coherent across the ... Continued The post AI-Ready Private Cloud with Cisco and VMware appeared first on VMware Blogs. ## Optimize vLLM speculative decoding with FastMTP heads DevFeed: [Optimize vLLM speculative decoding with FastMTP heads]() Original publisher: [Read original article]() Author: Rahul Tuli Published: 2026-09-08T14:20:16Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [vllm](), [Inference](), [Large Language Model](), [Fine-tuning](), [model-deployment](), [deepseek](), [qwen]() Tags: [ai-inference](), [artificial-intelligence](), [data](), [deepseek](), [developer-tools](), [fine-tuning](), [hardware](), [inference](), [large-language-model](), [production](), [qwen](), [tokens](), [vllm]() ### AI overview This article explains how FastMTP-style fine-tuning improves vLLM speculative decoding. It describes using native multi-token prediction heads as speculators, adapting a single head for recursive multi-step drafting, extracting weights from verifier checkpoints, and producing vLLM-ready checkpoints without training from scratch. ### Source excerpt Autoregressive decoding makes large language model (LLM) inference memory-bandwidth bound: every token needs 1 full forward pass over billions of parameters, so the hardware spends most of its time moving weights rather than computing. MTP is a training objective: models like the DeepSeek and Qwen families learn to predict several future tokens at each position, which improves their data efficiency and quality. The post Optimize vLLM speculative decoding with FastMTP heads appeared first on Red Hat Developer. ## CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference DevFeed: [CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:50:55Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Cloud Native Ecosystem](), [Inference](), [AI Platform](), [model-deployment](), [distributed-systems](), [Back end]() Tags: [ai](), [ai-infrastructure](), [announcements](), [backend](), [china](), [cloud](), [cloud-native-ecosystem](), [developers](), [distributed-systems](), [inference](), [production](), [research]() ### AI overview CNCF and SlashData report that cloud native adoption in China is growing, including among IIoT and younger backend developers. The research describes cloud native infrastructure as supporting AI teams' transition from experimentation and training to production serving and distributed inference. ### Source excerpt New research finds China's IIoT developers (48%) outpace the global average (42%) in cloud native adoption as AI infrastructure matures Key Highlights: SHANGHAI - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China --Sept. 8,... ## Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy DevFeed: [Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy]() Original publisher: [Read original article]() Author: Sana Fayyaz Published: 2026-09-07T07:01:34Z Content type: article Language: en Sources: [Red Hat Developer]() Topics: [quantization](), [Large Language Model](), [GPU](), [Inference](), [model-deployment](), [Post-training optimization](), [llama](), [LLM Techniques]() Tags: [ai-inference](), [artificial-intelligence](), [compression](), [deployment](), [gpu](), [inference](), [language-models](), [large-language-models](), [llama](), [llm](), [memory](), [performance](), [post-training](), [quantization]() ### AI overview This article explains INT8 W8A8 quantization for Llama 3.1 8B using SmoothQuant and GPTQ. It describes how quantization reduces memory requirements and can improve serving performance while preserving model accuracy, including a reported 46% reduction in model size and planned benchmark testing with vLLM and llm-compressor. ### Source excerpt Large language models are expensive to serve. A model like Llama 3.1 8B in Bfloat16 (BF16) precision occupies roughly 15 GB of GPU memory. In BF16, each of the 8 billion parameters takes 2 bytes to store, which adds up to roughly 15 GB for the weights--and that's not all. The GPU needs memory for the key-value (KV) cache to store context for active requests, alongside intermediate tensor outputs (activations, as we call them) generated during inference. The post Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy appeared first on Red Hat Developer. ## NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral DevFeed: [NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-04T18:01:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [hugging face](), [Nvidia](), [AI Development](), [AI Infrastructure](), [Application Development](), [datasets](), [Cloud](), [Deployment](), [model-deployment](), [inference-providers]() Tags: [ai](), [ai-infrastructure](), [application-development](), [cloud](), [creators](), [datasets](), [deployment](), [enterprise](), [hugging-face](), [inference-providers](), [models](), [multi-cloud](), [nvidia](), [platforms]() ### AI overview NVIDIA has agreed to acquire Hugging Face for $12.93 billion, with plans to expand its infrastructure and AI development capabilities. Hugging Face is expected to retain its brand and operate as an open, hardware-neutral platform supporting models, datasets, applications, multiple clouds, accelerators, and inference providers. ### Source excerpt NVIDIA has agreed to acquire Hugging Face for $12.93 billion, a transaction that would extend the company's position from accelerated compute and AI infrastructure into one of the industry's most widely used platforms for open models, datasets, and application development. In an announcement published on the NVIDIA website, CEO Jensen Huang said the company plans The post NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral appeared first on StorageReview.com. ## Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson DevFeed: [Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-04T16:21:04Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [model-deployment](), [Inference](), [Inference Performance](), [Optimization](), [Mixture of Experts (MoE)]() Tags: [agentic-ai-generative-ai](), [edge](), [edge-computing](), [how-to](), [inference](), [inference-performance](), [jetpack](), [jetson](), [jetson-orin](), [mixture-of-experts-moe](), [nvfp4](), [optimization](), [physical-ai](), [reasoning](), [thor](), [tutorial](), [vllm]() ### AI overview A tutorial on deploying and optimizing compact reasoning and agentic AI models on NVIDIA Jetson. It covers choosing models, improving inference with NVFP4 quantization and speculative decoding, serving example models with vLLM, and validating a configuration for a workload. ### Source excerpt Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run... ## Cut GPU inference cold start from 8 minutes to less than a minute DevFeed: [Cut GPU inference cold start from 8 minutes to less than a minute]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-03T18:30:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [GPU](), [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [CUDA](), [Optimization](), [Amazon S3](), [Kubernetes]() Tags: [ai-infrastructure](), [amazon-eks](), [aws-marketplace](), [cpu](), [cuda](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [optimization](), [post-contributed](), [s3](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview The article measures GPU model startup from pod creation to the first inference response and identifies six sequential phases. It reports that CUDA kernel recompilation dominates startup for a 64 GB model, while S3 weight downloads dominate for a 203 GB model. Configuration and platform changes reduced warm-node startup times by 80-93%, with additional cold-node improvements requiring Amazon EKS Auto Mode. ### Source excerpt We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold start from 8 minutes to less than a minute appeared first on The New Stack. ## Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo DevFeed: [Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-25T20:57:54Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Large Language Model](), [Inference](), [model-deployment](), [Nvidia](), [Optimization](), [CUDA](), [Processes](), [GPU](), [NCCL](), [sglang]() Tags: [agentic-ai-generative-ai](), [cuda](), [cuda-x](), [data-science](), [deployment](), [developer-tools-techniques](), [gpu](), [inference](), [llm](), [models](), [nccl](), [nvidia](), [optimization](), [process]() ### AI overview This article explains NVIDIA Dynamo's shadow engine recovery for LLM inference. A fully initialized shadow engine shares weights with the active engine and takes over after a process failure, reducing recovery from a 283-second cold restart to 7.3 seconds in the described two-worker deployment. ### Source excerpt When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,... ## The full stack behind abundant intelligence DevFeed: [The full stack behind abundant intelligence]() Original publisher: [Read original article]() Published: 2026-08-25T07:05:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [Hardware](), [Low-Latency Inference](), [Inference Performance](), [model-deployment](), [gpt-oss](), [Benchmark](), [cloud-infrastructure](), [systems](), [Amazon Web Services](), [Microsoft]() Tags: [ai](), [aws](), [benchmark](), [company](), [compute](), [cost](), [efficiency](), [energy-efficiency](), [hardware](), [inference](), [low-latency-inference](), [microsoft](), [models](), [nvidia](), [openai]() ### AI overview OpenAI describes an integrated compute strategy spanning data centers, chips, models, software, products, and devices. It reports that its custom Jalapeño inference chip achieved higher peak throughput per kilowatt and lower token latency than commercial systems on the InferenceX benchmark using GPT-OSS 120B, while also performing strongly on DeepSeek R1 and Kimi K2. ### Source excerpt OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost. ## Preloading Knowledge Into a Model Instead of Retrieving It DevFeed: [Preloading Knowledge Into a Model Instead of Retrieving It]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-24T17:13:39Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Retrieval Augmented Generation (RAG)](), [Caching](), [long-context](), [Inference](), [Self-hosted](), [model-deployment](), [GPU]() Tags: [caching](), [compute](), [context-window](), [deployment](), [inference](), [rag](), [retrieval](), [self-hosted](), [storage]() ### AI overview This article introduces a three-part RAG Systems course about preloading a knowledge corpus into a model's stored KV cache so subsequent queries can skip retrieval, chunking, and embedding. It covers naive, compressed, modular, and trained preloading, along with context limits, provider and self-hosted economics, cache compression constraints, and production deployment. ### Source excerpt How to process your corpus once, skip retrieval entirely, and serve every query from a stored cache. Three parts covering the full spectrum. ## Mistral x HUMAIN DevFeed: [Mistral x HUMAIN]() Original publisher: [Read original article]() Published: 2026-08-24T16:02:41Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Strategy](), [AI Infrastructure](), [AI Models](), [Model Development](), [model-deployment](), [Localization (l10n)](), [datacenter](), [Cybersecurity]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [arabic](), [data](), [data-center](), [deployment](), [enterprise](), [europe](), [financial-services](), [manufacturing](), [model-development](), [public-sector](), [security]() ### AI overview Mistral and HUMAIN announce a strategic collaboration to advance sovereign AI in Saudi Arabia and across the Middle East. The initiative covers AI infrastructure, advanced model development, localized Arabic-capable models, and deployment of AI solutions for regulated industries. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Open-weight models are fast on Neon AI Gateway. Here's why DevFeed: [Open-weight models are fast on Neon AI Gateway. Here's why]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-20T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [databricks](), [Inference Performance](), [model-deployment](), [Low-Latency Inference](), [Caching](), [Mixture of Experts (MoE)](), [gpt-oss](), [Large Language Model]() Tags: [caching](), [databricks](), [gpt-oss](), [inference](), [mixture-of-experts-moe](), [optimization](), [performance](), [product](), [speed]() ### AI overview The article explains why open-weight models can deliver low latency on Neon AI Gateway when paired with Databricks Foundation Model APIs and an optimized serving stack. It highlights continuous batching, KV-cache paging, custom kernels, quantization, multi-GPU layouts, prompt caching, and Mixture-of-Experts architectures. ### Source excerpt Open-weight models are built for performance, but how they're served determines whether end users actually feel that speed. Neon AI Gateway calls Databricks Foundation Model APIs for Databricks-hosted open-weight models, a stack that has years of inference engineering behind it. ## Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost DevFeed: [Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost]() Original publisher: [Read original article]() Author: Cody Mazza-Anthony Published: 2026-08-19T14:32:58Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Compression](), [Large Language Model](), [Embeddings](), [Latency](), [Inference](), [Inference Performance](), [Low-Latency Inference](), [Post-training optimization](), [GraphQL](), [model-deployment]() Tags: [compression](), [embedding](), [inference](), [latency](), [llm](), [model](), [tokens]() ### AI overview Gisting compresses an LLM agent's system prompt into learned gist tokens, preserving prediction quality while reducing inference latency, increasing throughput, and lowering GPU requirements. ### Source excerpt Gisting compresses context into a set of learned tokens, preserving its quality while making the model faster and cheaper. ## Securing the Infrastructure of Intelligence DevFeed: [Securing the Infrastructure of Intelligence]() Original publisher: [Read original article]() Author: 黄仁勋 Published: 2026-08-17T12:34:51Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [cloud-infrastructure](), [Nvidia](), [OpenAI](), [Inference](), [model-deployment](), [Cloud]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [cloud](), [compute](), [corporate](), [frontier-ai](), [inference](), [infrastructure](), [model](), [nvidia](), [openai](), [platform](), [products](), [resource](), [resources](), [revenue](), [scale](), [supply-chain](), [systems]() ### AI overview NVIDIA describes a partnership with SB Energy to secure land, power and services capacity for an AI factory at the PORTS-Pike Technology Campus in Ohio, where OpenAI will be the tenant. OpenAI is expected to build and operate the facility using NVIDIA's DSX AI factory platform, with an initial capacity of 4.25 gigawatts. ### Source excerpt AI factories are the defining infrastructure of the AI era -- where compute transforms energy and data into intelligence that powers every business, industry and country. In the AI economy, compute is revenue. AI factories require a full stack of critical resources: advanced chips, packaging, memory and networking -- as well as land, power and [...] ## Introducing memory retention for agentic memory in OpenSearch DevFeed: [Introducing memory retention for agentic memory in OpenSearch]() Original publisher: [Read original article]() Author: Erfan Ballew Published: 2026-08-13T22:29:25Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [AI Agent](), [audit trail](), [model-deployment]() Tags: [agentic](), [audit-trail](), [blog](), [compliance](), [cost](), [experimental](), [lifecycle](), [memory](), [opensearch](), [precision](), [retention](), [storage](), [technical]() ### AI overview This article explains OpenSearch 3.8's experimental memory retention feature for agentic memory. It describes age-based and count-based limits for different memory types, how policies prevent stale context and uncontrolled storage growth, and how to enable retention on an existing cluster. ### Source excerpt Learn how the memory retention policy in OpenSearch automatically manages the lifecycle of agentic memory, controlling storage growth while preserving specific memories. The post Introducing memory retention for agentic memory in OpenSearch appeared first on OpenSearch. [Next page]()